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Radiomics and artificial intelligence analysis of CT data for the identification of prognostic features in multiple myeloma

본 논문은 다발성 골수종(MM)의 골격 침범을 정량화하고, 이를 기반으로 예후 바이오마커를 자동으로 도출하는 새로운 영상‑기반 접근법을 제시한다. 연구의 핵심은 두 가지 가설이다. 첫째, 기존에 CLL(만성 림프구성 백혈병)에서 검증된 ‘골내 부피(intra‑osseous volume, IBV)’가 MM에서도 질병 진행을 판단하는 신뢰할 수 있는 지표가 될 수 있다는 점; 둘째, 골밀도 조직의 국소 병변에서 추출한 방사선학적 특징이 환자 계층화에 활용될 수 있다는 점이다. 데이터 및 전처리 후향적 설계로 25명의 MM 환자와 102

Radiomics and artificial intelligence analysis of CT data for the identification of prognostic features in multiple myeloma

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